深度神经网络在金融决策中表现优异，可提升风险管理和投资预测能力。
A Comprehensive Review: Applicability of Deep Neural Networks in Business Decision Making and Market Prediction Investment
- 融合多模态数据的深度神经网络框架提升金融预测性能
- 在风险管控、组合优化与算法交易中实现显著效果
- 适合金融工程、量化投资及企业决策研究者参考
大数据（包括结构化和非结构化）给经济与商业领域带来前所未有的挑战。如何组织、分类并分析此类数据以获取有意义的洞察，一直是企业领导者与学术研究者关注的核心课题。本文综述了深度神经网络在经济业务决策与投资预测中的最新应用，尤其聚焦于风险管理、投资组合优化和算法交易。尽管存在数据隐私与跨市场分析的限制，研究显示深度神经网络在金融分类与预测任务中表现突出。此外，通过整合不同数据模态的多个神经网络，可构建更稳健、高效且可扩展的金融预测框架。
原文摘要 · Abstract (English)
Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever-going research topics for business leaders and academic researchers. This paper studies recent applications of deep neural networks in decision making in economical business and investment; especially in risk management, portfolio optimization, and algorithmic trading. Set aside limitation in data privacy and cross-market analysis, the article establishes that deep neural networks have performed remarkably in financial classification and prediction. Moreover, the study suggests that by compositing multiple neural networks, spanning different data type modalities, a more robust, efficient, and scalable financial prediction framework can be constructed.
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